Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/16/2024 and 3/9/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/21/2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 6, 8, 12-13, 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Yash et al. “Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification.” International Conference on Medical Imaging with Deep Learning (2021) in view of Pao (United States Patent Application Publication US 2025/0104450 A1) and Hohne et. al. (United States Patent Application Publication US 2025/0265832 A1).
Regarding claim 1, Sharma et. al. discloses a method for training an image recognition model, performed by a computer device, the method comprising (Sharma et. al. Figure 1): obtaining a sample image and a corresponding sample label; segmenting the sample image into a sample image patch bag of sample image patches corresponding to the sample image (Sharma et. al., Fig.1(a), section 3.1 “For digital pathology classification problems, WSIs (W) of patients are available along with their disease labels.”), the sample patch bag having a bag label corresponding to the sample label of the sample image (Sharma et. al. Fig.1(b)-(c), section 3.1 “Hence, using the Otsu thresholding approach and sliding window approach, patches containing substantial tissue area (>50%) of desirable size are extracted. Given a WSI W (bag) with label y, we extract w1, w2, w3, …, wn patches (instances) from it for training.”); predicting an attention distribution of the sample image patch bag from the bag feature and the plurality of patch features by the attention layer in the image recognition model (Sharma et. al. Fig. 1(d)-(f), section 3.2 “We used the weighted-average aggregation approach proposed in Ilse et. al. (2018) for aggregating the patch-level representation to obtain WSI-level representations.”, section 3.4 “Using the aggregated representation of the WSI and representation of patches (instances), end-to-end training is performed using cross-entropy and KL-divergence loss. Along with WSI and patch cross-entropy loss, for each cluster, KL-divergence loss between the patches’ attention weight and a uniform distribution is included.”); determining a relative entropy loss corresponding to the sample patch bag based on a difference the attention distribution predicted and an expected distribution corresponding to the bag label of the sample patch bag, the attention distribution being a distribution obtained by predicting image content in the sample patch bag, and the expected distribution being a distribution of the sample patch bag indicated by the bag label (Sharma et. al. Fig. 1(d)-(f), section 3.2 “We used the weighted-average aggregation approach proposed in Ilse et. al. (2018) for aggregating the patch-level representation to obtain WSI-level representations.”, section 3.4 “Using the aggregated representation of the WSI and representation of patches (instances), end-to-end training is performed using cross-entropy and KL-divergence loss. Along with WSI and patch cross-entropy loss, for each cluster, KL-divergence loss between the patches’ attention weight and a uniform distribution is included.”); determining a first cross entropy loss corresponding to the sample patch bag based on a difference between the bag label and the corresponding bag feature analysis result of the sample patch bag; for each sample image patch of the sample image patches in the sample patch bag: performing feature analysis on the sample image patch by using the second fully connected layer in the image recognition model to obtain a patch feature analysis result of the sample image patch; determining a second cross entropy loss corresponding to the sample image patch based on a difference between a patch label of the sample image patch and the corresponding patch analysis result of the sample image patch (Sharma et. al. Fig.1 (g), section 3.4 “…the instance representation are passed through Gy’: h [Wingdings font/0xE0] y’ to obtain patches prediction probability. Instance loss is included with weak supervision assumption. Along with WSI and patch cross-entropy loss, for each cluster, KL-divergence loss between the patches’ attention weight and a uniform distribution is included.”); performing weighted fusion on the relative entropy loss, the first cross entropy loss, and the plurality of second cross entropy losses to obtain a total loss value of the image recognition model (Sharma et. al. section 3.4 “The instance representation are passed through Gy’: h[Wingdings font/0xE0]y’ to obtain patches prediction probability…Instance loss is included with weak supervision assumption”); and training the first fully connected layer, the second fully connected layer, and the attention layer in the image recognition model simultaneously based on the total loss value, the trained image recognition model being configured to recognize image content in an image (Sharma et.al. loss L(Gy, Gy’, Ga, Ge) in section 3.4).
However, Sharma et. al. fails to disclose wherein the image recognition model includes a first fully connected layer, a second fully connected layer that is communicatively coupled to the first fully connected layer, and an attention layer that is communicatively coupled to both the first fully connected layer and the second fully connected layer, processing the sample image patch bag by the first fully connected layer in the image recognition model to obtain a bag feature corresponding to the sample image patch bag; processing the sample image patches in the sample image patch bag by the first fully connected layer in the image recognition model to obtain a plurality of patch features corresponding to the sample image patches in the sample image patch bag; generating a fused feature of the sample image patch bag from the attention distribution of the sample image patch bag, the bag feature and the plurality of patch features; processing the fused feature of the sample image patch bag by using the second fully connected layer to obtain a bag feature analysis result of the sample patch bag indicating a recognition result of the image content in the sample patch bag.
Pao teaches wherein the image recognition model includes a first fully connected layer, a second fully connected layer that is communicatively coupled to the first fully connected layer, and an attention layer that is communicatively coupled to both the first fully connected layer and the second fully connected layer, processing the sample image patch bag by the first fully connected layer in the image recognition model to obtain a bag feature corresponding to the sample image patch bag; processing the sample image patches in the sample image patch bag by the first fully connected layer in the image recognition model to obtain a plurality of patch features corresponding to the sample image patches in the sample image patch bag (Pao Abstract, [0005], [0010], [0022], Fig 9A: attention weights).
PNG
media_image1.png
602
508
media_image1.png
Greyscale
Hohne et. al. teaches generating a fused feature of the sample image patch bag from the attention distribution of the sample image patch bag, the bag feature and the plurality of patch features; processing the fused feature of the sample image patch bag by using the second fully connected layer to obtain a bag feature analysis result of the sample patch bag indicating a recognition result of the image content in the sample patch bag (Hohne et. al. [0156]: These regional features are fused with the local features of selected patches, and optionally patches adjacent to the selected patches, to form a global embedding. [0025]-[0033]: computing a loss based on a difference between the class to which the global embedding is assigned and the class to which the training image is assigned, modifying parameters of the first machine learning model based on the computed loss).
These features are important to the claimed invention because the architecture of the machine learning model via attention neural network improves the accuracy based on the three different types of entropy losses. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Sharma et. al., Pao and Hohne et. al., so these features are included in the solution of the claimed invention.
Regarding claim 8, which is a computer device, comprising a processor and a memory, the memory having at least one program stored therein that, when executed by the processor, causes the computer device to implement a method for training an image recognition model of claim 1, which the rejection analysis is incorporated herein.
Regarding claim 15, which is a non-transitory computer-readable storage medium, having at least one program stored thereon that, when executed by a processor of a computer device, causes the computer device to implement a method for training an image recognition model of claim 1, which the rejection analysis is incorporated herein.
Regarding claim 6, Sharma et. al. further discloses the method according to claim 5, wherein the method further comprises: using, in response to the sample image patches in the sample patch bag belonging to a same sample image, a sample label corresponding to the sample image as a bag label corresponding to the sample patch bag; and determining, in response to the sample image patches in the sample patch bag belonging to different sample images, the bag label corresponding to the sample patch bag based on the patch labels corresponding to the sample image patches (Sharma et. al. section 3.4 “The aggregated representation is passed through Gy: z[Wingdings font/0xE0]y to obtain WSI prediction probability).
Regarding claim 12, Sharma et. al. further discloses the computer device according to claim 8, wherein the segmenting the sample image into a sample image patch bag of sample image patches corresponding to the sample image comprises: segmenting an image region of the sample image to obtain the sample image patches; and allocating sample image patches belonging to a same sample image to a same bag to obtain the sample patch bag (Sharma et. al. Fig. 1 (d)-(g), section 3.2 “weighted-average aggregation approach…to obtain WSI-level representations.” And section 3.4 “KL-divergence loss between the patches’ attention weight and a uniform distribution is included. The aggregated representation is passed through Gy: y[Wingdings font/0xE0] z to obtain WSI prediction probability”).
Regarding claim 13, Sharma et. al. further discloses the computer device according to claim 12, wherein the method further comprises: using, in response to the sample image patches in the sample patch bag belonging to a same sample image, a sample label corresponding to the sample image as a bag label corresponding to the sample patch bag; and determining, in response to the sample image patches in the sample patch bag belonging to different sample images, the bag label corresponding to the sample patch bag based on the patch labels corresponding to the sample image patches (Sharma et. al. Fig. 1 (d)-(g), section 3.2 “weighted-average aggregation approach…to obtain WSI-level representations.” And section 3.4 “KL-divergence loss between the patches’ attention weight and a uniform distribution is included. The aggregated representation is passed through Gy: y[Wingdings font/0xE0] z to obtain WSI prediction probability”).
Claim(s) 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma, Yash et al. “Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification.” International Conference on Medical Imaging with Deep Learning (2021), Pao (United States Patent Application Publication US 2025/0104450 A1) and Hohne et. al. (United States Patent Application Publication US 2025/0265832 A1) as applied to claims 1 and 15 above, in further view of Jewsbury, Robert et al. “A QuadTree Image Representation for Computational Pathology.” 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (2021): 648-656. (Year: 2021).
Regarding claim 5, Sharma et. al., Pao, and Hohne et. al. disclose the method according to claim 1. However, Sharma et. al., Pao and Hohne et. al. fail to disclose wherein the segmenting the sample image into a sample image patch bag of sample image patches corresponding to the sample image comprises: segmenting an image region of the sample image to obtain the sample image patches; and allocating sample image patches belonging to a same sample image to a same bag to obtain the sample patch bag.
Jewsbury et. al. teaches wherein the segmenting the sample image into a sample image patch bag of sample image patches corresponding to the sample image comprises: segmenting an image region of the sample image to obtain the sample image patches; and allocating sample image patches belonging to a same sample image to a same bag to obtain the sample patch bag (Jewsbury et. al. 2.2 MIL framework, where generated collection of down-sampled image regions are given labels either positive or negative based on categories of features).
PNG
media_image2.png
533
1142
media_image2.png
Greyscale
Separating the sample images into different categories based on features that are extracted are critical to the claimed invention. This defines the solution to the problem. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Sharma et. al., Pao, Hohne et. al. and Jewsbury so that this solution is implemented fully.
Regarding claim 19, Sharma et. al., Pao, and Hohne et. al. disclose the non-transitory computer-readable storage medium according to claim 15. However, Sharma et. al., Pao, and Hohne et. al. fail to disclose wherein the segmenting the sample image into a sample image patch bag of sample image patches corresponding to the sample image comprises: segmenting an image region of the sample image to obtain the sample image patches; and allocating sample image patches belonging to a same sample image to a same bag to obtain the sample patch bag.
Jewsbury et. al. teaches wherein the segmenting the sample image into a sample image patch bag of sample image patches corresponding to the sample image comprises: segmenting an image region of the sample image to obtain the sample image patches; and allocating sample image patches belonging to a same sample image to a same bag to obtain the sample patch bag (Jewsbury et. al. 2.2 MIL framework, where generated collection of down-sampled image regions are given labels either positive or negative based on categories of features).
PNG
media_image2.png
533
1142
media_image2.png
Greyscale
Separating the sample images into different categories based on features that are extracted are critical to the claimed invention. This defines the solution to the problem. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Sharma et. al., Pao, Hohne et. al. and Jewsbury so that this solution is implemented fully.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA YIFANG LIN whose telephone number is (571)272-6435. The examiner can normally be reached M-F 7:00am-6:15pm, with optional day off.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSICA YIFANG LIN/Examiner, Art Unit 2668 September 14, 2026
/VU LE/Supervisory Patent Examiner, Art Unit 2668
Prosecution Insights